Transcript 1) NOIR: What is the level of your data?

Learning Approach
Research Methods
Question 1
• While at school Thomas noticed an increase in
aggressive behaviour at break time when
more people were outside. This is an example
of
• A negative correlation
• B no correlation
• C positive correlation
Answer 1
• While at school Thomas noticed an increase in
aggressive behaviour at break time when
more people were outside. This is an example
of
• A negative correlation
• B no correlation
• C positive correlation – Why is this… (PTO)
Answer 1 - Correlations
• Correlation is a statistical measurement of the relationship
between two variables.
• Possible correlations range from +1 to –1.
• A zero correlation indicates that there is no relationship
between the variables.
• A correlation of –1 indicates a perfect negative correlation,
meaning that as one variable goes up, the other goes
down.
• A correlation of +1 indicates a perfect positive correlation,
meaning that both variables move in the same direction
together – So Thomas notices an increase in aggressive
behaviour at break time when there are more people
outside (why might that be?)
Question 2
• Which sampling method gives an equal
chance of a participant being selected?
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A
B
C
D
Opportunity
Random
Self-selected
Volunteer
Answer 2
• Which sampling method gives an equal
chance of a participant being selected?
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A
B
C
D
Opportunity
Random – Why?
Self-selected
Volunteer
Answer 2 – Random sampling
• A random sample is a subset of individuals
that are randomly selected from a population.
• Because researchers usually cannot obtain
data from every single person in a group, a
smaller portion is randomly selected to
represent the entire group as a whole.
• The goal is to obtain a sample that is
representative of the larger population.
Answer 2 task
• What are the strengths and weaknesses of
random sampling?
Answer 2 task
• What are the strengths and weaknesses of random sampling?
• Random sampling is the best technique for providing an unbiased
representative sample of a target population.
• Random sampling can be very time consuming and is often impossible to
carry out, particularly when you have a large target population, of say all
students.
• For example if you do not have the names of all the people in your target
population you would struggle to conduct a random sample.
• If you ask people to volunteer for a study the sample is already not
random as some people may be more or less likely to volunteer for
things.
• Similarly if you decided to put out an advert for participants it would be
almost impossible to guarantee that every member of your target
population has an equal chance of viewing the advert.
Answer 2 task
• What are the strengths and weaknesses of
opportunity sampling?
Answer 2
• What about the others –
Opportunity sampling
• Opportunity sampling is the sampling technique most used by
psychology students.
• It consists of taking the sample from people who are available at
the time the study is carried out and fit the criteria your are looking
for.
• This may simply consist of choosing the first 20 students in your
college canteen to fill in your questionnaire.
• The main advantage of opportunity sampling are that it is quick and
easy as the sample already exists.
• The main disadvantage is that the opportunity sample is biased
because the members of it have self selected and are all similar in
at least one way, therefore any results will only be truly
generalisable to that specific group of people
Answer 2 task
• What are the strengths and weaknesses of
self-selected sampling?
Answer 2
• What about the others –
Self-selected
• Self selected sampling (or volunteer sampling) consists of
participants becoming part of a study because they volunteer when
asked or in response to an advert.
• This sampling technique is used in a number of the core studies, for
example Milgram (1963).
• This technique, like opportunity sampling, is useful as it is quick and
relatively easy to do.
• It can also reach a wide variety of participants.
• However, the type of participants who volunteer may not be
representative of the target population for a number of reasons.
• For example, they may be more obedient, more motivated to take
part in studies and so on.
Question 3
IV is the independent variable and DV is the dependant
variable. Identify which of the following statements is
correct.
• A The IV is manipulated to see the effect on the DV.
• B The DV is manipulated to see the effect on the IV.
• C The IV is kept constant so it does not affect the
results.
• D The DV is kept constant so it does not affect the
results.
Answer 3
IV is the independent variable and DV is the dependent
variable. Identify which of the following statements is
correct.
• A The IV is manipulated to see the effect on the DV.
• B The DV is manipulated to see the effect on the IV.
• C The IV is kept constant so it does not affect the
results.
• D The DV is kept constant so it does not affect the
results.
Question 4
An independent groups design is when
A - Different participants take part in different experimental
conditions.
B - Different participants are matched and they take part in different
conditions.
C - The same participants take part in all the experimental conditions.
D - Half the participants do condition 1 first then condition 2, the
other half do condition 2 first then condition 1.
Answer 4
An independent groups design is when:
A - Different participants take part in different experimental
conditions.
B - Different participants are matched and they take part in different
conditions. (matched pairs design)
C - The same participants take part in all the experimental conditions.
(repeat measures)
D - Half the participants do condition 1 first then condition 2, the
other half do condition 2 first then condition 1. (repeat measures)
Answer 4 task
• What are the strengths and weaknesses of an
independent group design?
Answer 4 task
• What are the strengths and weaknesses of an independent group
design?
• Avoids order effects (such as practice or fatigue) as people
participate in one condition only.
• If a person is involved in several conditions they may become
bored, tired and fed up by the time they come to the second
condition, or becoming wise to the requirements of the
experiment!
• More people are needed than with the repeated measures design
(i.e. more time consuming).
• Differences between participants in the groups may affect results,
for example; variations in age, sex or social background.
• These differences are known as participant variables (i.e. a type of
extraneous variable).
Answer 4 task
Matched pairs design:
• What is it?
• What are the strengths and weaknesses?
Answer 4 task
Matched pairs design:
• One pair must be randomly assigned to the experimental group and
the other to the control group.
• Reduces participant (i.e. extraneous) variables because the
researcher has tried to pair up the participants so that each
condition has people with similar abilities and characteristics.
• Avoids order effects, and so counterbalancing is not necessary.
• Very time-consuming trying to find closely matched pairs.
• Impossible to match people exactly, unless identical twins!
Answer 4 task
Repeat measures:
• What is it?
• What are the strengths and weaknesses?
Answer 4 task
Repeat measures:
• The same participants take part in each condition of the independent
variable. This means that each condition of the experiment includes the
same group of participants.
• Pro: Fewer people are needed as they take part in all conditions (i.e. saves
time)
• There may be order effects. Order effects refer to the order of the
conditions having an effect on the participants’ behavior.
• Performance in the second condition may be better because the
participants know what to do (i.e. practice effect).
• Or their performance might be worse in the second condition because
they are tired (i.e. fatigue effect).
Some questions on inferential
statistics:
Tests of difference
Level of measurement
Participant design
Nominal data
Ordinal data
Interval/ratio data
Repeated measures or matched
pairs
Sign test
Wilcoxon Matched
Independent groups
chi-squared test
Mann-Whitney
Related t test*
Unrelated t test*
Tests for relationship (correlations)
Ordinal data
Interval/ratio data
Spearman’s Rank Correlation Co-efficient
Pearson’s Product Moment
Correlation Co-efficient*
e.g. if you have ordinal data with independent measures design and you’re looking for
a difference, you will use Mann-Whitney ‘U.’
Question 5
• In order to carry out a Mann Whitney U Test on data which
two of the following statements must be correct?
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A The data can be nominal.
B The data can be ordinal.
C The design must be correlational.
D The experimental design must be independent groups.
E The experimental design must be matched pairs.
F The experimental design must be repeated measures.
Question 5 Answer
• In order to carry out a Mann Whitney U Test on data which
two of the following statements must be correct?
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A The data can be nominal.
B The data can be ordinal.
C The design must be correlational.
D The experimental design must be independent groups.
E The experimental design must be matched pairs.
F The experimental design must be repeated measures.
When choosing a test there are three
things to consider:
1) NOIR: What is the level of your data?
• Nominal Data
• Ordinal Data
• Interval and Ratio
When choosing a test there are three
things to consider:
1) NOIR: What is the level of your data?
• How many people like running?
• Sprinting?
• Weight training?
When choosing a test there are three
things to consider:
1) NOIR: What is the level of your data?
• How many people like running?
• Sprinting?
• Weight training?
• Nominal Data!
When choosing a test there are three
things to consider:
1) NOIR: What is the level of your data?
• Using a likert scale rate the following out of 5
(5 being the highest) of importance to the
human body
• running?
• Sprinting?
• Weight training?
When choosing a test there are three
things to consider:
1) NOIR: What is the level of your data?
• Using a likert scale rate the following out of 5 (5
being the highest) of importance to the human
body
• running?
• Sprinting?
• Weight training?
• Ordinal Data (ranking some type of order)
When choosing a test there are three
things to consider:
1) NOIR: What is the level of your data?
• The order established just then is not specific
enough.
• Therefore we may consider them in terms of
interval/ratio data:
- Running (burns 200 calories in 30 mins)
• Sprinting (burns 400 calories in 30 mins)
• Weight training (burns 600 calories in 30 mins)
Nominal Data:
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The simplest thing a number can do.
It can tell us how many things there are!
Example - nominal data is a headcount or a tally.
It doesn’t tell us if something is bigger, brighter or bolder, just how
many.
For example - show of hands; how many people in the class study
English.
Your head count provides nominal data.
If you were replicating Piaget’s research at a primary school you
might count the number of five year olds who can successfully
complete the three mountains task and compare this to the
number of seven year olds (a great study!).
Give some examples of this type of data…
Ordinal Data:
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Allows us to put things in order.
For example ‘A’ might be more attractive than ‘B’ but uglier than ‘C’.
We have the order ‘C’ ‘A’ ’B’ in terms of attractiveness.
Crucially however, we can’t be sure that the difference between ‘C’ and ‘A’
is the same as the difference between ‘A’ and ‘B’.
‘C’ and ‘A’ might both be very attractive whereas ‘B’ might be less
attractive.
We can’t tell that the intervals are the same.
Usain Bolt won the men’s 200m at Beijing, Shawn Crawford was second
and Walter Dix third.
From this we can’t tell if the difference between first and second was the
same as the difference between second and third.
First, second, third provides ordinal data.
Give some examples of this type of data…
Interval and Ratio:
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Allows us to put things in order (ascending or descending) just as ordinal, however
this time we can be sure that the intervals are the same.
We know that the difference between 10cm and 11cm is the same as the
difference between 15cm and 16cm.
The same applies to weight or mass, temperature and time.
An odd one to consider is IQ.
Some psychologists believe it yields interval/ratio data, others that it is merely
ordinal. What do you think?
Generally speaking if you need a piece of equipment to measure it, then its
interval or ratio.
For the purposes of statistics interval and ratio are taken as the same.
There is however, a subtle difference.
Ratio has a true zero.
So no minus values, e.g. time, weight, height.
Interval data can be minus e.g. temperature in degrees Celcius.
As a result you can say that 20cm is twice as long as 10cm. You cannot say 20C is
twice as hot as 10C.
When choosing a test there are three
things to consider:
2) Correlation or difference?
Correlation or difference?
• The issue - The relationship between attractiveness and
punishment.
• This could be done either way:
• You could produce an ascending scale of attractiveness and
compare this to the level of punishment given to each person. You
would predict a negative correlation; as attractiveness increases,
level of punishment given decreases.
• Alternatively you could split your photographs into two groups,
with the beautiful people in one group and less beautiful in the
other. Then count the level of punishment offered for each. You
are now looking for a difference between the two groups.
When choosing a test there are three
things to consider:
3) Repeat or independent measures design?
Repeated or independent measures
design:
• If you’re using the same group of participants to
assess both variables its repeated measures.
• If the participants in one condition differ from
the other its independent.
• There are times when the decision is made for
you.
• Sex differences, age differences, cultural
differences…
• They have to be different participants in each
condition.
Tests of difference
Level of measurement
Participant design
Nominal data
Ordinal data
Interval/ratio data
Repeated measures or
matched pairs
Sign test
Wilcoxon Matched
Independent groups
chi-squared test
Mann-Whitney
Related t test*
Unrelated t test*
Tests for relationship (correlations)
Ordinal data
Nominal
Interval/ratio data
Spearman’s Rank Correlation Co-efficient
chi-squared test
Pearson’s Product Moment
Correlation Co-efficient*
e.g. if you have ordinal data with independent measures design and you’re looking for
a difference, you will use Mann-Whitney ‘U.’
Decision time
Having decided on the above three dimensions, use the chart below to decide which test to
use. You will be expected to know about three: Chi squared, Mann-Whitney ‘U’ and Spearman’s
‘rho.’
Test of difference
Test of correlation
(relationship)
Type of Data
Repeated Measures
/ matched Pairs
Independent
measures / single
participant
Nominal
Sign Test
Chi Squared
Chi Squared
Ordinal
Wilcoxon sign test
Mann Whitney ‘U’
Spearman ‘rho’
Related ‘t’ test
Independent
(unrelated) ‘t’ test
Pearson product
moment (‘r’)
Interval/
ratio
e.g. if you have ordinal data with independent measures design and you’re looking for
a difference, you will use Mann-Whitney ‘U.’
Inferential statistics:
• At the end of this you’ll have calculated ONE
number.
• This number will tell you whether your results
are meaningful and statistically significant, or
whether they’ve more than likely occurred by
chance and are little more than a fluke.
• These are your Critical and observed values.
Critical and observed values
• The number you calculate is your observed value.
• This needs to be compared with the critical value in the
appropriate table.
• Each test has its own table with various critical values depending on
the level of significance (5% (0.05), 1% (0.01), 0.5% (0.005)) of the
results being due to chance.
• With Spearman’s rho and chi squared tests the number you
calculate needs to be equal to or greater than the critical value for
your findings to be significant.
• ‘Spearman’s rho’ and ‘chi squared’ both contain ‘Rs’ as does the
word gReater
• ‘Mann Whitney U’ test does not contain an R so with this test the
critical value needs to be equal to or smaller than the critical value.
Question 6
• A study was carried out to investigate whether
kicking off from the starting block with the right
foot or left foot gave sprinters an advantage.
• 20 participants were asked to take part in two
sprints; in one trial they kicked off with their left
foot and in another with their right.
• It was found that on average kicking off with their
right foot gave them an advantage of 80 ms
(milliseconds).
• Give a non-directional (two-tailed) experimental
hypothesis for the study.
Help:
• A one tailed hypothesis specifies a directional relationship
between groups.
• Here we are saying that we expect children in Samoa to be
further from their mothers than children in Belize.
• We not only state that there will be differences between
the groups but we specify in which direction the differences
will exist.
• Anytime we expect a relationship to be directional (i.e. to
go one specific way) we are using a one-tailed hypothesis.
• This is the opposite of a two-tailed hypothesis.
• A two tailed hypothesis would predict that there was a
difference between groups, but, would make no reference
to the direction of the effect.
Question 6 Answer
• 2 marks for an appropriate experimental hypothesis. Partial mark if
only the IV or the DV given.
A non-directional (two-tailed) hypothesis must be given. 0 marks
for a directional or a null hypothesis.
• There is a difference in the time taken to sprint/eq; (1 mark)
• There is a difference between whether it was the left or right
foot/eq; (1 mark)
• There will be a difference in the speed/time taken from the starting
blocks, depending which foot the sprinters use/eq; (2 marks)
• Either moving off with the left foot or the right foot will lead to a
decrease in the time taken to push off the starting blocks/eq; (2
marks)
• Look for other reasonable ways of expressing a hypothesis.
Question 7
• State the design used in the study.
Question 7 Answer:
• State the design used in the study.
• Reject methods or ‘same participants’.
If more than one answer given accept the first
one.
• repeated measures
• related design
• Within groups design
• related [single word only]
• repeated [single word only]
Question 8:
• State the independent variable (IV) for the
study.
Question 8:
• State the independent variable (IV) for the
study.
• The IV gets 1 mark.
The IV is which foot they kick off from the
starting block with. Just the word foot isn’t
enough, they have to mention left and right,
or ‘which foot’ to get the mark.
• Look for other reasonable ways of expressing
the IV.
Question 9:
• Researchers carried out a correlational study to
see if there was a relationship between eating
breakfast and students’ scores on a math test.
• They carried out a Spearman’s rho test on the
data and found that the observed value of rho
was +0.519, N = 20.
• Table to show the critical values for Spearman’s
test
N=20
p≤0.05
p≤0.025
0.380
0.447
• What is meant by the term p≤0.05?
Question 9 Answer:
• A full answer must refer to the probability the results are due to
chance and the answer must also refer to the correct probability
(1/20, 5%). This can be expressed either as a figure, or as 5%, for 2
marks.
• The results are significant at 5%/eq; 1 mark
• p<0.05 means that we can be at least 95% confident that the results
did not occur by chance/eq; 1 mark.
• The probability the results are due to chance is equal or less than to
5%/ 5/100/ 1/20 /eq; 2 marks
• There is a 95% or more probability the results are not due to
chance/eq; 2 marks.
• The probability the results are due to chance is equal or less than
5/100 /eq; 2 marks.
• There is a 1/20 or less probability the results are due to chance/eq;
two marks.
Question 10:
• State whether the researchers would reject
their null hypothesis.
Question 10 Answer:
• State whether the researchers would reject their
null hypothesis.
• 0 marks for accept.
A one word answer is acceptable.
• • Reject/eq;
• Reject the null hypothesis/eq;
• Yes/eq;
• Look for other reasonable ways of expressing
this answer.
Question 11:
• Explain your answer to (b)(i) above.
Question 11:
• Explain your answer to (b)(i) above.
• T.E. - If 12bi is blank or incorrect and bii clearly refers to the rejection of
the null hypothesis then max marks can be given.
• A full answer must refer to the observed value and the critical value and
must mention the actual figures. One mark for a weak answer and two
marks when an answer is elaborated.
• The observed value is bigger than the critical value/eq; 1 mark.
• 0.519 is bigger than 0.38/eq; 1 mark.
• the observed value of rho is greater than the critical value at both .05 and
.01 so it would not matter which level of significance was chosen so the
null would be rejected/eq: 2 marks
• The observed value of 0.519is bigger than the critical value of
0.38/0.447/eq; 2 marks.
• The critical value is 0.38/0.447 which is smaller than the observed value of
0.519/eq; 2 marks.
Question 12 (10 marks):
• For part of your course you will have carried out a
practical in the Psychodynamic Approach which
was a correlation.
• Describe the procedure of your practical, and
evaluate your practical. You may evaluate your
investigation in terms of:
• • validity
• reliability
• credibility
• generalisability .